Secure Semantic Encoding: Since semantic information can reflect the real data distribution of users to a certain extent and is also vulnerable to privacy leakage in communication, we need to protect the semantic information transmitted by users. For example, Chen et al. [1]} proposed a federated learning framework in semantic communication systems, which can protect the privacy of the system. Yang et al. [2]} proposed a federated learning-based semantic communication framework to cope with the computing and communication overhead under protecting the privacy of the system. The above work illustrates that federated learning can be applied to wireless communication systems to protect the devices' data privacy. Therefore, Tong et al. [3]} designed a wav2vec-based autoencoder federated semantic communication framework, and it can significantly reduce transmission error and heavy communication overhead. Due to the low efficiency of the current semantic communication system, we focus on how to extract semantic information effectively and effectively adapt to different goals.
